Since we want
- Each variable as a separate column
- Each row as a separate observation
We would want to reshape a table like:
| Account | Checking | Savings |
|---|---|---|
| “12456543” | 8500 | 8900 |
| “12283942” | 6410 | 8020 |
| “12839485” | 78000 | 92000 |
Into a table that looks more like:
| Account | Account Type | Amount |
|---|---|---|
| “12456543” | “Checking” | 8500 |
| “12456543” | “Savings” | 8900 |
| “12283942” | “Checking” | 6410 |
| “12283942” | “Savings” | 8020 |
| “12839485” | “Checking” | 78000 |
| “12839485” | “Savings” | 920000 |
We can use pd.melt() to do this transformation. .melt() takes in a DataFrame, and the columns to unpack:
The parameters you provide are:
frame: the DataFrame you want tomeltid_vars: the column(s) of the old DataFrame to preservevalue_vars: the column(s) of the old DataFrame that you want to turn into variablesvalue_name: what to call the column of the new DataFrame that stores the valuesvar_name: what to call the column of the new DataFrame that stores the variables
The default names may work in certain situations, but it’s best to always have data that is self-explanatory. Thus, we often use .columns() to rename the columns after melting:
Instructions
Print out the columns of students.
There is a column for the scores on the fractions exam, and a column for the scores on the probabilities exam.
We want to make each row an observation, so we want to transform this table to look like:
| full_name | exam | score | gender_age | grade |
|---|---|---|---|---|
| “First Student” | “Fractions” | score% | … | … |
| “First Student” | “Probabilities” | score% | … | … |
| “Second Student” | “Fractions” | score% | … | … |
| “Second Student” | “Probabilities” | score% | … | … |
| … | … | … |
Use pd.melt() to create a new table (still called students) that follows this structure.
Print the .head() and the .columns of students.
Also, print out the .value_counts() of the column exam.